Mill load parameter soft measurement method

A load parameter and soft-sensing technology, applied in the field of soft-sensing, can solve problems such as long training time, over-fitting small sample modeling, etc., and achieve high accuracy

Inactive Publication Date: 2015-09-23
中央军委联合参谋部第五十五研究所
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Problems solved by technology

The partial least squares (KPLS) method can overcome the shortcomings of BPNN such as long training time, overfitting and difficulty in small sample modeling.

Method used

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  • Mill load parameter soft measurement method

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Embodiment Construction

[0029] The present invention is described below based on examples, but the present invention is not limited to these examples. In the following detailed description of the invention, some specific details are set forth in detail. The present invention can be fully understood by those skilled in the art without the description of these detailed parts. In order not to obscure the essence of the present invention, well-known methods, procedures, procedures, components and circuits have not been described in detail.

[0030] Additionally, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0031] Unless the context clearly requires, throughout the specification and claims, "comprises", "comprises" and similar words should be interpreted in an inclusive sense rather than an exclusive or exhaustive meaning; that is, "including but not limited to" meaning.

[0032] In the descr...

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Abstract

The invention discloses a mill load parameter soft measurement method, which comprises the steps of firstly decomposing mill cylinder vibrations and vibration sound signals into sub-signals (intrinsic mode functions (IMFs)) with different time scales and different physical meanings by adopting an ensemble empirical mode decomposition (EEMD) technology; then selecting of three kinds of features of the multi-scale IMF by adopting mutual information (MI) based adaptive feature selection approach, wherein the three kinds of features are the frequency spectrum, the marginal spectrum, and the mean and the standard deviation of the instantaneous amplitude and the frequency of Hilbert transformation; and finally, constructing a soft measurement model based on selective integration kernel partial least squares (KPLS) method on the basis of selected spectrum features and a training sample. A small ball mill based simulation experiment result shows that the method disclosed by the invention can effectively detect load parameters.

Description

technical field [0001] The invention relates to the field of soft measurement, in particular to a method for soft measurement of mill load parameters. Background technique [0002] The optimal operation control of the grinding process needs to accurately detect the load parameters in the mill (see literature [1] P.Zhou, T.Y.Chai, H.Wang, "Intelligent optimal-setting control for grinding circuits of mineral processing," IEEE Transactions on Automation Science and Engineering, 6(2009) 730-743. and literature [2] T.Y.Chai, "Operational optimization and feedback control for complex industrial processes," Acta Automatica Sinica, 39(2013) 1744-1757). Tens of thousands of steel balls are arranged in layers inside the mill, and the impact forces of different layers of steel balls on the materials inside the mill and the barrel of the mill have different intensities and periods. Usually the measured cylinder vibration signal is a mixture of multiple sub-signals with different time s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/401
CPCG05B19/401
Inventor 汤健柴天佑刘卓丁进良贾美英李东
Owner 中央军委联合参谋部第五十五研究所
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